Trade Information Research Articles

Exchange rate innovation, global pandemic and stock market returns: Empirical evidence from ECOWAS countries

This study used the panel VAR impulse response function model and high-frequency monthly data from 2020M1 to 2021M12 to examine the response of innovations in the exchange rate and stock market returns to the COVID-19 pandemic in Economic Communities of West African States (ECOWAS). The study found that the effect of shocks of COVID-19 today on future exchange rate innovation worsens the real conditions of exchange rate innovation in ECOWAS countries. Again, the impact of the shocks of COVID-19 today on future stock market returns encourages the real conditions of stock market returns in ECOWAS countries. It was recommended that instead of lockdown, ECOWAS governments should explore other policy options peculiar to the region in COVID-19 containment and also strive harder to deepen the stock market and encourage increased utilization of information and communication technology in stock market trading to cut costs, raise returns and connect trade with the rest of the world’s stock markets.

Option Pricing Based on REGARCH Model with High-Frequency Information

As a prevalent tool for hedging risk, the trading volume of options has been growing increasingly in the derivatives market. Precision in the estimation of volatility leads to accurate option pricing. Since volatility is time-varying and has a clustering effect, GARCH class of volatility models is effective in modeling volatility precisely. This paper utilizes the realized EGARCH (REGARCH) model combined with Monte Carlo simulation to investigate the role of high-frequency information in option pricing. The parameter estimates of the REGARCH model are obtained via joint maximum likelihood estimation using observations on returns and realized measure. Applying the model to S&P options market, the empirical results show that the REGARCH model that using high-frequency data is more efficient than the model that only use daily closing prices, including the EGARCH, NGARCH and GJR-GARCH models. This paper demonstrates that incorporating realized measures into volatility models can improve the accuracy of option pricing. The REGARCH model contained more intraday trading information from high-frequency data, can measure the additional risk premiums and specific volatility shocks.

Is the Taiwan Stock Market (Swarm) Intelligent?

It is well-believed that most trading activities tend to herd. Herding is an important topic in finance. It implies a violation of efficient markets and hence, suggests possibly predictable trading profits. However, it is hard to test such a hypothesis using aggregated data (as in the literature). In this paper, we obtain a proprietary data set that contains detailed trading information, and as a result, for the first time it allows us to validate this hypothesis. The data set contains all trades transacted in 2019 by all the brokers/dealers across all locations in Taiwan of all the equities (stocks, warrants, and ETFs). Given such data, in this paper, we use swarm intelligence to identify such herding behavior. In particular, we use two versions of swarm intelligence—Boids and PSO (particle swarm optimization)—to study the herding behavior. Our results indicate weak swarm among brokers/dealers.

Design of agricultural question answering information extraction method based on improved BILSTM algorithm

With the rapid growth of the agricultural information and the need for data analysis, how to accurately extract useful information from massive data has become an urgent first step in agricultural data mining and application. In this study, an agricultural question-answering information extraction method based on the BE-BILSTM (Improved Bidirectional Long Short-Term Memory) algorithm is designed. Firstly, it uses Python’s Scrapy crawler framework to obtain the information of soil types, crop diseases and pests, and agricultural trade information, and remove abnormal values. Secondly, the information extraction converts the semi-structured data by using entity extraction methods. Thirdly, the BERT (Bidirectional Encoder Representations from Transformers) algorithm is introduced to improve the performance of the BILSTM algorithm. After comparing with the BERT-CRF (Conditional Random Field) and BILSTM algorithm, the result shows that the BE-BILSTM algorithm has better information extraction performance than the other two algorithms. This study improves the accuracy of the agricultural information recommendation system from the perspective of information extraction. Compared with other work that is done from the perspective of recommendation algorithm optimization, it is more innovative; it helps to understand the semantics and contextual relationships in agricultural question and answer, which improves the accuracy of agricultural information recommendation systems. By gaining a deeper understanding of farmers’ needs and interests, the system can better recommend relevant and practical information.

Ambiguity and informativeness of (non-)trading

We study a sequential trading mechanism with ambiguity-averse agents modeled by multiple prior preferences. Informed traders generally mix between trading and non-trading, and their trading probability decreases with ambiguity. If agents are sufficiently ambiguous, informed traders do not trade, and only noise traders place orders; trading becomes uninformative. When signal accuracy is ambiguous, trading can make public beliefs more ambiguous over time, which leads to social learning failures in the long run. Moreover, since informed traders may not trade, no-trade can also be informative when signal accuracy is asymmetric. Even with continuous action spaces, sufficiently high ambiguity stops social learning.

Ghanaian Small and Medium-Scale Enterprises (SMEs') Readiness for African Continental Free Trade Area (AfCFTA) Implementation

This paper seeks to assess Ghanaian Small and Medium-Scale Enterprises (SMEs') readiness for the African Continental Free Trade Area (AfCFTA) Implementation. The research instrument used to collect data was questionnaires and the study employed descriptive research design. The study randomly selected 250 SMEs from a population of 480 SMEs that were registered members of the Ghana National Chamber of Commerce and Industry, Cape Coast Branch and operating within manufacturing, pharmaceutical, herbal, tourism, hospitality, food and beverage, agribusiness, garment, leather, and textiles. Data collected from the SMEs was analysed using SPSS, version 28. The findings from the study shows that, there is a significant gap between Ghanaian SMEs' readiness for AfCFTA implementation. The study finds out that although majority of SMEs are capable of innovation and developing new products, they also have limited knowledge of export documentation, trade information and infrastructure, trade finance, and funding for expansion. They also have low knowledge and lack a basic understanding of the benefits of AfCFTA, its advantages, components, protocols, and Rules of Origin. The study again revealed that the obstacles facing Ghanaian SMEs exportation are high cost of production, border delays, poor infrastructure, high inflation, difficulty in accessing credit, and language problems. Based on these findings, the study recommended that SMEs needs to be retooled particularly in the area of AfCFTA Rules of Origin, its objectives, components, and Ghana’s trade policy to improve their readiness for AfCFTA implementation. Again, businesses should concentrate more on value addition, and be dedicated to looking for ways to enhance their current or existing products to outperform comparable products from other African nations.

The phenomenon of China’s carbon accounting policies and practices

Purpose This study aims to explore China’s carbon accounting policies and practices to enhance its carbon accounting and trading practices and achieve carbon neutrality. Design/methodology/approach This study adopts a phenomenological methodology to explore carbon accounting by setting the scope of the phenomenon, establishing research methods, conducting interviews with 30 participants from 14 emitting entities and four accounting firms and establishing methods for data analysis. Five themes were identified using a five-step coding method. Findings The results show no uniformity in the accounting treatment of carbon trading; however, common characteristics exist, and they have generally adopted simplified and conservative accounting methods under China’s emission trading scheme. Their common characteristics are that the granted allowances are not recognised as assets by most emitting entities, emissions allowance assets are measured using the historical cost method, most emitting entities only conduct accounting when there are actual cash flows or outflows with regard to carbon emissions allowance trading, and only a few emitting entities disclose carbon trading information in their financial statements or corporate social responsibility reports. Originality/value This study highlights accountants’ crucial role in carbon activities by guiding emitting entities’ low-carbon initiatives and culture and transforming managers’ mindsets. It also introduces the China Accounting Standards Committee-based accounting methods for allowances and obligations, aiding carbon disclosure quality and sustainability. These findings also inform the carbon accounting standards that may enhance corporate accountability and address climate change.

A Systematic Review on Impact of Political Crisis and Election on Capital Market Returns

The capital markets worldwide are vastly impacted by market sentiments and investors tend to react and rebalance their portfolios time to time due to such changes in market sentiments from political events. This systematic review examines the impact of political crisis and election on the capital market returns worldwide. It employs a systematic review research method and has gone through 4573 articles, out of which 62 articles published in reputed journals between 1970 and 2023 were considered for review. Of the methodology of the studies reviewed, 43.5% is quantitative, 27.4% is mixed, 17.7% is qualitative, and the rest is conceptual. Researches published in the context of Asian countries contributed 40% of this reviewed study, followed by Europe, Africa, and North America, each with 10%, South America with 3%, Australia with 1%, and allied countries within unions or agreements with 26%. The results show that political crises significantly impact capital market returns in Asia. Studies in North American context indicate that before elections and during political crises, the capital market volatility significantly rises with decreased capital market returns. In Australia, political events cause financial market fluctuation. In Africa, political instability hinders long-term capital accumulation. Additionally, informal political instability in South America directly affects economic growth, while formal instability indirectly affects economic vulnerability. The outcomes of such research hold practical implications for policymakers in formulating portfolio strategies and, therefore, delineate avenues for research exploring the integrated effects of political events on capital market returns by amalgamating trading information from stock exchanges worldwide.

Corruption and insider trading

We investigate firm corruption in China by extracting a measure of corruption from published financial statements and use this to demonstrate that corruption impacts the trading decisions of insiders. Specifically, we show that insiders in firms that are more corrupt trade more aggressively, and they are more willing to trade on their private information as evidenced by the increased informativeness of their trades, in respect of both purchases and sales. This link between firm corruption and trade informativeness is robust to the inclusion of a number of factors that are known to influence the informativeness of such trades, including trade characteristics, insider characteristics and the firm's information environment. We also consider the effect of the appointment of a new CEO or Chair. Overall, corruption related trade informativeness holds consistently for both purchases and sales. Finally, we show that this measure of corruption is robust to the inclusion of several alternative indicators of corporate misconduct.

Digital Privacy Challenges in China: Human Flesh Search

The advent of digital technology has brought unprecedented convenience but also significant challenges to personal privacy, particularly in China. The phenomenon of human flesh search (HFS), facilitated by both legal internet platforms and illicit information trade, has become a source of major privacy violations. This paper explores the effectiveness of China's current legal measures, particularly the Criminal Law as Article 253(A), in safeguarding personal information against these threats. It also examines the implications of these legal shortcomings on individuals daily lives and privacy. The dangers of human searches and their impact on the reality level are analyzed through detailed profiling of the parties involved in cases related to cyber violence. This report analyzes these legal inadequacies, comparing them with international standards, and suggests enhancements to better safeguard personal privacy in the digital age. By learning from international practices and rigorously adapting and enforcing its laws, China can better safeguard its citizens against privacy violations and set a standard for personal information protection in the digital era.

High-Frequency Quantitative Trading of Digital Currencies Based on Fusion of Deep Reinforcement Learning Models with Evolutionary Strategies

High-frequency quantitative trading in the emerging digital currency market poses unique challenges due to the lack of established methods for extracting trading information. This paper proposes a deep evolutionary reinforcement learning (DERL) model that combines deep reinforcement learning with evolutionary strategies to address these challenges. Reinforcement learning is applied to data cleaning and factor extraction from a high-frequency, microscopic viewpoint to quantitatively explain the supply and demand imbalance and to create trading strategies. In order to determine whether the algorithm can successfully extract the significant hidden features in the factors when faced with large and complex high-frequency factors, this paper trains the agent in reinforcement learning using three different learning algorithms, including Q-learning, evolutionary strategies, and policy gradient. The experimental dataset, which contains data on sharp up, sharp down, and continuous oscillation situations, was chosen to test Bitcoin in January-February, September, and November of 2022. According to the experimental results, the evolutionary strategies algorithm achieved returns of 59.18%, 25.14%, and 22.72%, respectively. The results demonstrate that deep reinforcement learning based on the evolutionary strategies outperforms Q-learning and policy gradient concerning risk resistance and return capability. The proposed approach offers a robust and adaptive solution for high-frequency trading in the digital currency market, contributing to the development of effective quantitative trading strategies.

Short selling and the pricing of PIN information risk

We present evidence that the pricing of the probability of informed trading (PIN) information risk varies substantially with stocks’ short selling environment. For lightly shorted stocks, their risk-adjusted returns (alphas) increase reliably with both the good news (PIN_G) and bad news (PIN_B) components of their PIN. The positive PIN-alpha relations decline and then disappear as stocks’ shorting activity increases. Our findings are consistently evident with shorting-interest, shorting-flow, and the probability of informed shorting, and are more prominent for smaller-cap stocks. Our findings support theories where asymmetric information with imperfectly competitive markets can impact stocks’ cost of equity.

P2P electricity trading model for urban multi-virtual power plants based on double-layer energy blockchain

The urban virtual power plant (VPP) enhances the overall performance of the urban power system by aggregating diverse distributed energy resources (DERs) to provide energy services. With the advancement of smart grid technology, peer-to-peer (P2P) electricity trading has emerged a new paradigm of electricity trading within urban VPPs. In this study, we propose a P2P electricity trading model for multi-VPPs in urban areas, based on a double-layer energy blockchain framework. Initially, users submit their electricity trading information for matching at the user layer; any remaining unfulfilled electricity demand is then further matched at the VPP layer. The user participation process in trading encompasses several stages: trading start, matching, validation, execution, and settlement. Additionally, this study introduces a double-layer blockchain structure consisting of one main chain and multiple side chains. The main chain records real-time trading information from the VPP layer while the side chains are dedicated to recording real-time trading information from the user layer. The feasibility of the proposed model is demonstrated through its implementation in Hyperledger Fabric. Experimental results indicate that the proposed model effectively reduces urban users' reliance on the main grid and decreases their electricity expenses.

Insider Trading Restrictions and Informed Trading in Peer Stocks

Using a uniquely constructed data set of trades by corporate insiders in all stocks, we find that, after insider trading regulations become stricter, insiders are 20% more likely to trade in peer stocks and that such trades become more profitable. The increase in both the probability and profitability of peer-stock trades is driven by the insider’s information that is fungible to industry peers. Stricter insider trading laws are designed to improve liquidity and price informativeness in capital markets. We show that peer trading dampens these intended benefits of the insider trading regulation. This paper was accepted by Ranjani Krishnan, accounting. Funding: This work was supported by NSE-NYU Stern Initiative on the Study of Indian Financial Markets. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02907 .

Regionalizing the supply chain in process life cycle inventory with multiregional input–output data: An implementation for ecoinvent with EXIOBASE

AbstractLife cycle inventory (LCI) databases, such as ecoinvent, are crucial for life cycle assessment (LCA), but lack country‐specific resolution in activity details and trade between regional activities in the supply chain. For example, ecoinvent only provides rest‐of‐world and European datasets for photovoltaic panel production, consuming the same global consumption mix of photovoltaic cells. Global or continental activities and trade limit the use of existing country activities, such as electricity production, and the accuracy and granularity of LCA results. We addressed this issue by disaggregating ecoinvent's global and continental unit processes to the country level and by regionalizing trade using country‐sector‐specific consumption mixes of product origins from the multiregional input–output table EXIOBASE. This produced a consistently regionalized ecoinvent at country level. Comparing the climate change impacts of 195,708 non‐market datasets in our regionalized ecoinvent with reference datasets in ecoinvent reveals manufacturing sectors as the most affected. The study of photovoltaic panel production shows that the differences in climate change impacts are mainly due to the different country‐specific suppliers of components and their electricity mixes, which are lost in the aggregated original data. The water consumption impacts of wheat production show the country differences only when regionalized CFs are applied, indicating that regionalized biosphere and technosphere flow amounts are needed for better results. The mapping quality between ecoinvent and EXIOBASE and inconsistent market definitions were discussed along with further limitations. Our study highlights the potential of incorporating better trade information and regional disaggregation in LCI databases to improve the LCA outcomes.

A Neuro-Driven Mobile Robot Enhanced by LoRa and EEG Technology

The Cognitive Rover project presents an imaginative way to deal with portable mechanical technology, coordinating high level neuro-innovations with long-range correspondence capacities to make a flexible and versatile mechanical stage. The essential target of the task is to foster a versatile robot fit for independent route and connection with its current circumstance, directed by brain signals got through electroencephalography (EEG) innovation. Utilizing the LoRa (Long Range) correspondence convention, the robot lays out strong network overstretched distances, empowering far off activity and information transmission in different situations, including search and salvage missions, perilous climate investigation, and far off review errands. The proposed framework design comprises of two fundamental parts: a neuro-driven control framework and a correspondence module. The neuro-driven control framework uses EEG signals gathered from a MindWave headset to decipher client orders and natural boosts. Through AI calculations and continuous sign handling strategies, the framework makes an interpretation of EEG signals into noteworthy orders for independent route, obstruction evasion, and undertaking execution. The correspondence module incorporates LoRa innovation to work with consistent information trade between the robot and far off administrators, empowering continuous observing, control, and video real time over significant distances. Key benefits of the Cognitive Rover incorporate its versatility to assorted conditions, instinctive client association through cerebrum PC interface innovation, and vigorous long-range correspondence abilities. Additionally, the task adds to progressions in mechanical technology, human-robot cooperation, and assistive advancements, with expected applications in misfortune reaction, ecological checking, and foundation review. Key Words: Electroencephalography (EEG), LoRa (Long Range), Cognitive Rover (CR), Brain Computer Interface (BCI), Bluetooth Low Energy (BLE), Transmitter (Tx)

Temporal Implicit Multimodal Networks for Investment and Risk Management

Many deep learning works on financial time-series forecasting focus on predicting future prices/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single-task, and/or unimodal settings. Forecasting for investment and risk management involves multiple tasks in multivariate settings: forecasts of expected returns and risks of assets in portfolios, and correlations between these assets. As different sources/types of time-series influence future returns, risks, and correlations of assets in different ways, it is also important to capture time-series from different modalities. Hence, this article addresses financial time-series forecasting for investment and risk management in a multivariate, multitask, and multimodal setting. Financial time-series forecasting, however, is challenging due to the low signal-to-noise ratios typical in financial time-series, and as intra-series and inter-series relationships of assets evolve across time. To address these challenges, our proposed Temporal Implicit Multimodal Network (TIME) model learns implicit inter-series relationship networks between assets from multimodal financial time-series at multiple time-steps adaptively. TIME then uses dynamic network and temporal encoding modules to jointly capture such evolving relationships, multimodal financial time-series, and temporal representations. Our experiments show that TIME outperforms other state-of-the-art models on multiple forecasting tasks and investment and risk management applications.

Focal digital devices and meaning in digital education: An ethnographic study of Ghanaian schools

Although extant research has addressed the importance of focal things and practices, as well as human values when engaging with digital technologies, a lot remains to be understood about the role of digital focal things in digital education. The paper argues that while Albert Borgmann understood digital devices as objects that render meaningless engagement, based on an ethnographic study of junior high schools in the Central region of Ghana, digital focal things can indeed invite meaningful learning experiences in digital education. However, despite positive gains there are also other aspects to this media ecology that raise tensions such as the trading and selling of student information and students becoming surveillance commodities. This paper begins with an overview of Borgmann’s philosophy, followed by an outline of methods and then moves to an analysis of digital focal things in the context of junior high schools in Ghana. The paper ends with concluding remarks.

Trade-war Tariffs and Supply Chain Trade

Abstract Although Chinese exports to the United States hit an all-time high in 2022, it would be incorrect to conclude that the U.S.–China trade war had no effects on trade. By exploiting fine product-level information on trade war tariffs, this paper demonstrates how the trade war diminished China's exports to the United States in tariff-impeded products, while expanding opportunities for exports from other countries in a fashion that had some systematic characteristics. First, the impact of the trade war tariffs was not immediate, as trade responses took place with a lag. Second, the responses to trade-war tariffs were heterogenous across countries and regions, with finer distinctions by product categories. Finally, trade that could be viewed as highly tied to global value chains (GVCs), such as trade in information and communication technology products, exhibited stronger reactions to the imposition of trade-war tariffs. These responses, and their implications for the organization of global supply chains, are discussed considering current trade concerns.

An Empirical Analysis of Global Stock Markets Evidence from USA, Malaysia and Switzerland

This article investigates a unique dataset that includes detailed trading information of three countries stock indices from 2000 to 2022. First, to find that the stock markets of all three countries declined in 2008, due to the financial crisis at that time. Second, calculating the monthly rate of return of three countries and produce the annual portfolio performance outcomes that include returns, volatilities and Sharpe ratios. Third, calculating the maximum drawdowns, and further analyze the market risks of each country. Finally, combining multiple countries stock markets into one portfolio and showcase the portfolio optimization benefits.